Safe driving continues to be a major issue addressed by automobile manufacturers. With the development of more “telematics” technology in cars, there are increasing risks for drivers to be distracted. (The term “car” and its constituent grammatical forms can be understood herein as relating to automobiles and other commercially sold and distributed vehicles normally associated with private use, such as sedans, coupes, SUV's, minivans, pickups, etc.)
Normally, safe driving can be influenced by a large number of factors. The following are but a few examples:
a) Distractions from telematics devices (e.g. navigation system, telephones, radio controls, window controls)
b) Impaired driver states such as fatigue, drowsiness, and inattention.
c) Distracting processes (e.g. talking, applying the brakes).
d) Stress on the vehicle (e.g. speed, acceleration) and environmental characteristics (e.g., weather, positions of other cars).
e) Stress on the driver (e.g. drivers becoming involved in several tasks, such as making U-turns, trying to remember which voice command can turn off the radio, getting a cellular telephone to dial a call, etc.)
There are also plans in conjunction with telematics that would allow to drivers to communicate between themselves while they drive, that is, between one driver and another.
The known technology to reduce the risks described above includes a workload manager that has information, from different car sensors, about how burdened the driver may be at a given point in time. This technology allows, for example, for the blocking of an incoming telephone ring in a car if the driver presses brakes or turns the car. A primary disadvantage of these technologies is that they do not attenuate the risks presented to other drivers who may be near or passing a car where another driver is busy with playing games, listening to books or performing a telephone conversation. It would thus appear to be helpful at times to inform a driver about such risks associated with drivers in other cars.
In some countries, it is required that if drivers are younger than 17 then a mark is provided on the car to indicate this. In Russia (at least in Soviet times), it was required that if a driver is hearing impaired then information to the effect was placed on the back of the window in his or her car. For the most part, these methods are not sufficient. First, the markings or signs can be seen only when a driver in another car actually looks in their direction. Secondly, such labels are not dynamic and, thus, reflective only of a particular, fixed situation (such as the age of a driver). A need has thus been recognized in connection with providing a more dynamic arrangement for highlighting a variety of potentially dangerous situations to drivers of other cars and for ensuring that drivers of other cars do not have to actually look at a car (that presents risks) in order to get such information.
Among the efforts presented in this general direction, U.S. Pat. No. 6,236,968 (“Sleep prevention dialog based car system”) suggests fighting drowsiness by detecting drowsiness via speech biometrics and, if needed, by increasing arousal via speech interactivity. However, this method is highly limited in the context of attempting to solve all of the problems (a) through (e) outlined above. For example, inattention cannot be solved merely by interactive speech games, since a driver can easily play in speech game while simultaneously averting his/her attention from the road.
Other known methods are directed to the reduction of driver “workload” (or “cognitive load”). For example, some states prohibit the use of hand held telephones in cars by a driver. Some states even prevent telephone dialing if a driver has a high workload (e.g. accelerating, turning left) and/or if there is a heavy rain or fog. These rules are still not sufficient for safe driving overall since they do not cover other possible dangerous situations. Further, rules have not yet addressed all potentially dangerous driving situations since there are a very large number of factors that potentially affect safe driving, not all of which are yet well understood.
One of the ways to reduce driver cognitive workload is to allow the driver to speak naturally when interacting with a car system (e.g. when playing voice games, issuing commands via voice). It is difficult for a driver to remember a complex speech command menu (e.g. how to ask “What is the distance to JFK?” or “Or how far is JFK?” or “How long to drive to JFK?” etc.). This requires development of conversational interactive (CI) speech systems. CI speech systems can significantly improve a driver-vehicle relationship and contribute the driving safety. But the development of NLU (natural language understanding) for CI is the difficult problem.
One possible method for improving NLU is data collection. It is difficult to collect sufficient data that fully represents all possible ways how users might interact with CI system. But the problem with data collection is that no matter how large the data collection is, some users can produce some phrases that are not represented in the collected data nor in grammars that are developed from this data.
There is also a general assumption that the driver workload should not exceed a certain threshold in order that a driving could be safe but to date no well-established methods for measuring driver workload appear to have been suggested.